130 research outputs found
Proprioception and reaction for walking among entanglements
Entanglements like vines and branches in natural settings or cords and pipes
in human spaces prevent mobile robots from accessing many environments. Legged
robots should be effective in these settings, and more so than wheeled or
tracked platforms, but naive controllers quickly become entangled and stuck. In
this paper we present a method for proprioception aimed specifically at the
task of sensing entanglements of a robot's legs as well as a reaction strategy
to disentangle legs during their swing phase as they advance to their next
foothold. We demonstrate our proprioception and reaction strategy enables
traversal of entanglements of many stiffnesses and geometries succeeding in 14
out of 16 trials in laboratory tests, as well as a natural outdoor environment.Comment: Submitted to 2023 IEEE/RSJ International Conference on Intelligent
Robots and System
A novel plasticity rule can explain the development of sensorimotor intelligence
Grounding autonomous behavior in the nervous system is a fundamental
challenge for neuroscience. In particular, the self-organized behavioral
development provides more questions than answers. Are there special functional
units for curiosity, motivation, and creativity? This paper argues that these
features can be grounded in synaptic plasticity itself, without requiring any
higher level constructs. We propose differential extrinsic plasticity (DEP) as
a new synaptic rule for self-learning systems and apply it to a number of
complex robotic systems as a test case. Without specifying any purpose or goal,
seemingly purposeful and adaptive behavior is developed, displaying a certain
level of sensorimotor intelligence. These surprising results require no system
specific modifications of the DEP rule but arise rather from the underlying
mechanism of spontaneous symmetry breaking due to the tight
brain-body-environment coupling. The new synaptic rule is biologically
plausible and it would be an interesting target for a neurobiolocal
investigation. We also argue that this neuronal mechanism may have been a
catalyst in natural evolution.Comment: 18 pages, 5 figures, 7 video
Analysis of Actuator Control Strategies for Excitation of Intrinsic Modes in Compliant Robots with Series Elastic Actuators
In biology, body dynamics and elasticity in periodic motions most likely con-
tribute to efficiency, i.e., in mammalian locomotion. Likewise, elastic elements can be added to robotic systems in an attempt to mimic this biological concept. Compliant robots are less likely to get damaged after severe impacts and their mechanical energy storage via springs could be exploited for fast and explosive movements. In this thesis, we explore the question whether resonance excitation that solely considers link-side dynamics or also takes into account the motor inertia, can lead to an increase in performance in Series Elastic Actuator (SEA) driven robotic systems. We propose three different control approaches and compare them to compliant state-of-the-art control as baseline evaluation in simulation and hardware experiments. Moreover, we extend the investigation of motor-side-excitation with the aid of methods such as inertia shaping and simulative system variation. Experiment results regarding a pick-and-place task with fixed amplitude reveal that in the investigated test setup, it might not be beneficial to make dedicated use of the motor inertia. Instead, an approach that exclusively excites link-side dynamics appears, for this particular task and setup, to be advantageous. However, generally, also making use of the motor dynamics bears potential for specific investigations as it appears more flexible and the control behavior can be easily adapted. Thus, the presented thesis provides first fundamental insights about novel control strategies and lies the foundation for further systematic research with different actuation types and varying task goals
Robot Control for Task Performance and Enhanced Safety under Impact
A control law combining motion performance quality and low stiffness reaction to unintended contacts is proposed in this work. It achieves prescribed performance evolution of the position error under disturbances up to a level related to model uncertainties and responds compliantly and with low stiffness to significant disturbances arising from impact forces. The controller employs a velocity reference signal in a model-based control law utilizing a non-linear time-dependent term, which embeds prescribed performance specifications and vanishes in case of significant disturbances. Simulation results with a three degrees of freedom (DOF) robot illustrate the motion performance and self-regulation of the output stiffness achieved by this controller under an external force, and highlights its advantages with respect to constant and switched impedance schemes. Experiments with a KUKA LWR4+ demonstrate its performance under impact with a human while following a desired trajectory
Multi-contact Planning on Humans for Physical Assistance by Humanoid
International audienceFor robots to interact with humans in close proximity safely and efficiently, a specialized method to compute whole-body robot posture and plan contact locations is required. In our work, a humanoid robot is used as a caregiver that is performing a physical assistance task. We propose a method for formulating and initializing a non-linear optimization posture generation problem from an intuitive description of the assistance task and the result of a human point cloud processing. The proposed method allows to plan whole-body posture and contact locations on a task-specific surface of a human body, under robot equilibrium, friction cone, torque/joint limits, collision avoidance, and assistance task inherent constraints. The proposed framework can uniformly handle any arbitrary surface generated from point clouds, for autonomously planing the contact locations and interaction forces on potentially moving, movable, and deformable surfaces, which occur in direct physical human-robot interaction. We conclude the paper with examples of posture generation for physical human-robot interaction scenarios
์ธ๋ ๋ฐ ํ ํฌ ๋์ญํญ ์ ํ์ ๊ณ ๋ คํ ํ ํฌ ๊ธฐ๋ฐ์ ์์ ๊ณต๊ฐ ์ ์ด
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ์ตํฉ๊ณผํ๊ธฐ์ ๋ํ์ ์ตํฉ๊ณผํ๋ถ(์ง๋ฅํ์ตํฉ์์คํ
์ ๊ณต), 2021.8. ๋ฐ์ฌํฅ.The thesis aims to improve the control performance of the torque-based operational space controller under disturbance and torque bandwidth limitation. Torque-based robot controllers command the desired torque as an input signal to the actuator. Since the torque is at force-level, the torque-controlled robot is more compliant to external forces from the environment or people than the position-controlled robot. Therefore, it can be used effectively for the tasks involving contact such as legged locomotion or human-robot interaction. Operational space control strengthens this advantage for redundant robots due to the inherent compliance in the null space of given tasks. However, high-level torque-based controllers have not been widely used for transitional robots such as industrial manipulators due to the low performance of precise control. One of the reasons is the uncertainty or disturbance in the kinematic and dynamic properties of the robot model. It leads to the inaccurate computation of the desired torque, deteriorating the control stability and performance. To estimate and compensate the disturbance using only proprioceptive sensors, the disturbance observer has been developed using inverse dynamics. It requires the joint acceleration information, which is noisy due to the numerical error in the second-order derivative of the joint position. In this work, a contact-consistent disturbance observer for a floating-base robot is proposed. The method uses the fixed contact position of the supporting foot as the kinematic constraints to estimate the joint acceleration error. It is incorporated into the dynamics model to reduce its effect on the disturbance torque solution, by which the observer becomes less dependent on the low-pass filter design. Another reason for the low performance of precise control is torque bandwidth limitation. Torque bandwidth is determined by the relationship between the input torque commanded to the actuator and the torque actually transmitted into the link. It can be regulated by various factors such as inner torque feedback loop, actuator dynamics, and joint elasticity, which deteriorates the control stability and performance. Operational space control is especially prone to this problem, since the limited bandwidth of a single actuator can reduce the performance of all related tasks simultaneously. In this work, an intuitive way to penalize low performance actuators is proposed for the operational space controller. The basic concept is to add joint torques only to high performance actuators recursively, which has the physical meaning of the joint-weighted torque solution considering each actuator performance. By penalizing the low performance actuators, the torque transmission error is reduced and the task performance is significantly improved. In addition, the joint trajectory is not required, which allows compliance in redundancy. The results of the thesis were verified by experiments using the 12-DOF biped robot DYROS-RED and the 7-DOF robot manipulator Franka Emika Panda.๋ณธ ํ์ ๋
ผ๋ฌธ์ ์ธ๋๊ณผ ํ ํฌ ๋์ญํญ ์ ํ์ด ์กด์ฌํ ๋ ํ ํฌ ๊ธฐ๋ฐ ์์
๊ณต๊ฐ ์ ์ด๊ธฐ์ ์ ์ด ์ฑ๋ฅ์ ๋์ด๋ ๊ฒ์ ๋ชฉํ๋ก ํ๋ค. ํ ํฌ ๊ธฐ๋ฐ์ ๋ก๋ด ์ ์ด๊ธฐ๋ ๋ชฉํ ํ ํฌ๋ฅผ ์
๋ ฅ ์ ํธ๋ก์ ๊ตฌ๋๊ธฐ์ ์ ๋ฌํ๋ค. ํ ํฌ๋ ํ ๋ ๋ฒจ์ด๊ธฐ ๋๋ฌธ์, ํ ํฌ ์ ์ด ๋ก๋ด์ ์์น ์ ์ด ๋ก๋ด์ ๋นํด ์ธ๋ถ ํ๊ฒฝ์ด๋ ์ฌ๋์ผ๋ก๋ถํฐ ๊ฐํด์ง๋ ์ธ๋ ฅ์ ๋ ์ ์ฐํ๊ฒ ๋์ํ ์ ์๋ค. ๊ทธ๋ฌ๋ฏ๋ก ํ ํฌ ์ ์ด๋ ๋ณดํ์ด๋ ์ธ๊ฐ-๋ก๋ด ์ํธ์์ฉ๊ณผ ๊ฐ์ ์ ์ด์ ํฌํจํ๋ ์์
์ ์ํด ํจ๊ณผ์ ์ผ๋ก ์ฌ์ฉ๋ ์ ์๋ค. ์์
๊ณต๊ฐ ์ ์ด๋ ์ด๋ฌํ ํ ํฌ ์ ์ด์ ์ฅ์ ์ ๋ ๊ฐํ์ํฌ ์ ์๋๋ฐ, ๋ก๋ด์ด ์ฌ์ ์์ ๋๊ฐ ์์ ๋ ์์
์ ์๊ณต๊ฐ์์ ์กด์ฌํ๋ ๋ชจ์
๋ค์ด ๋ด์ฌ์ ์ผ๋ก ์ ์ฐํ๊ธฐ ๋๋ฌธ์ด๋ค. ๊ทธ๋ฌ๋ ์ด๋ฌํ ์ฅ์ ์๋ ๋ถ๊ตฌํ๊ณ ํ ํฌ ๊ธฐ๋ฐ์ ๋ก๋ด ์ ์ด๊ธฐ๋ ์ ๋ฐ ์ ์ด ์ฑ๋ฅ์ด ๋จ์ด์ง๊ธฐ ๋๋ฌธ์ ์ฐ์
์ฉ ๋ก๋ด ํ๊ณผ ๊ฐ์ ์ ํต์ ์ธ ๋ก๋ด์๋ ๋๋ฆฌ ์ฌ์ฉ๋์ง ๋ชปํ๋ค. ๊ทธ ์ด์ ์ค ํ ๊ฐ์ง๋ ๋ก๋ด ๋ชจ๋ธ์ ๊ธฐ๊ตฌํ ๋ฐ ๋์ญํ ๋ฌผ์ฑ์น์ ์กด์ฌํ๋ ์ธ๋์ด๋ค. ๋ชจ๋ธ ์ค์ฐจ๋ ๋ชฉํ ํ ํฌ๋ฅผ ๊ณ์ฐํ ๋ ์ค์ฐจ๋ฅผ ์ ๋ฐํ๋ฉฐ, ์ด๊ฒ์ด ์ ์ด ์์ ์ฑ๊ณผ ์ฑ๋ฅ์ ์ฝํ์ํค๊ฒ ๋๋ค. ์ธ๋์ ๋ด์ฌ ์ผ์๋ง์ ์ด์ฉํ์ฌ ์ถ์ ๋ฐ ๋ณด์ํ๊ธฐ ์ํด ์ญ๋์ญํ ๊ธฐ๋ฐ์ ์ธ๋ ๊ด์ธก๊ธฐ๊ฐ ๊ฐ๋ฐ๋์ด ์๋ค. ์ธ๋ ๊ด์ธก๊ธฐ๋ ์ญ๋์ญํ ๊ณ์ฐ์ ์ํด ๊ด์ ๊ฐ๊ฐ์๋ ์ ๋ณด๊ฐ ํ์ํ๋ฐ, ์ด ๊ฐ์ด ๊ด์ ์์น๋ฅผ ๋ ๋ฒ ๋ฏธ๋ถํ ๊ฐ์ด๊ธฐ ๋๋ฌธ์ ์์น์ ์ธ ์ค์ฐจ๋ก ๋
ธ์ด์ฆํด์ง๋ ๋ฌธ์ ๊ฐ ์์๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ๋ถ์ ํ ๊ธฐ์ ๋ก๋ด์ ์ํ ์ ์ด ์กฐ๊ฑด์ด ๊ณ ๋ ค๋ ์ธ๋ ๊ด์ธก๊ธฐ๊ฐ ์ ์๋์๋ค. ์ ์๋ ๋ฐฉ๋ฒ์ ๋ก๋ด์ ๊ณ ์ ๋ ์ ์ด ์ง์ ์ ๋ํ ๊ธฐ๊ตฌํ์ ์ธ ๊ตฌ์ ์กฐ๊ฑด์ ์ด์ฉํ์ฌ ๊ด์ ๊ฐ๊ฐ์๋ ์ค์ฐจ๋ฅผ ์ถ์ ํ๋ค. ์ถ์ ๋ ์ค์ฐจ๋ฅผ ๋์ญํ ๋ชจ๋ธ์ ๋ฐ์ํ์ฌ ์ธ๋ ํ ํฌ๋ฅผ ๊ณ์ฐํจ์ผ๋ก์จ ์ ์ญ ํต๊ณผ ํํฐ ์ฑ๋ฅ์ ๋ํ ์์กด๋๋ฅผ ์ค์ผ ์ ์๋ค. ํ ํฌ ๊ธฐ๋ฐ ์ ์ด์ ์ ๋ฐ ์ ์ด ์ฑ๋ฅ์ด ๋จ์ด์ง๋ ๋ ๋ค๋ฅธ ์ด์ ์ค ํ๋๋ ํ ํฌ ๋์ญํญ ์ ํ์ด๋ค. ํ ํฌ ๋์ญํญ์ ๊ตฌ๋๊ธฐ์ ์ ๋ฌ๋๋ ์
๋ ฅ ํ ํฌ์ ์ค์ ๋งํฌ์ ์ ๋ฌ๋๋ ํ ํฌ์์ ๊ด๊ณ๋ก ๊ฒฐ์ ๋๋ค. ํ ํฌ ๋์ญํญ์ ๊ตฌ๋๊ธฐ ๋ด๋ถ์ ํ ํฌ ํผ๋๋ฐฑ ๋ฃจํ, ๊ตฌ๋๊ธฐ ๋์ญํ, ๊ด์ ํ์ฑ ๋ฑ์ ์์ธ๋ค์ ์ํด ์ ํ๋ ์ ์๋๋ฐ ์ด๊ฒ์ด ์ ์ด ์์ ์ฑ ๋ฐ ์ฑ๋ฅ์ ๊ฐ์์ํจ๋ค. ์์
๊ณต๊ฐ ์ ์ด๋ ํนํ ์ด ๋ฌธ์ ์ ์ทจ์ฝํ๋ฐ, ๋์ญํญ์ด ์ ํ๋ ๊ตฌ๋๊ธฐ ํ๋๊ฐ ๊ทธ์ ์ฐ๊ด๋ ๋ชจ๋ ์์
๊ณต๊ฐ์ ์ ์ด ์ฑ๋ฅ์ ๊ฐ์์ํฌ ์ ์๊ธฐ ๋๋ฌธ์ด๋ค. ๋ณธ ์ฐ๊ตฌ์์๋ ์์
๊ณต๊ฐ ์ ์ด๊ธฐ์์ ์ฑ๋ฅ์ด ๋ฎ์ ๊ตฌ๋๊ธฐ์ ์ฌ์ฉ์ ์ ํํ๊ธฐ ์ํ ์ง๊ด์ ์ธ ์ ๋ต์ด ์ ์๋์๋ค. ๊ธฐ๋ณธ ์ปจ์
์ ์์
์ ์ด๋ฅผ ์ํ ํ ํฌ ์๋ฃจ์
์ ์ฑ๋ฅ์ด ์ข์ ๊ด์ ์๋ง ์ถ๊ฐ์ ์ผ๋ก ํ ํฌ ์๋ฃจ์
์ ๋ํด๋๊ฐ๋ ๊ฒ์ผ๋ก, ์ด๊ฒ์ ๊ฐ ๊ด์ ์ ๊ฐ์ค์น๊ฐ ๊ณ ๋ ค๋ ํ ํฌ ์๋ฃจ์
์ด ๋๋ ๊ฒ์ ์๋ฏธํ๋ค. ์ฑ๋ฅ์ด ๋ฎ์ ๊ตฌ๋๊ธฐ์ ์ฌ์ฉ์ ์ ํํจ์ผ๋ก์จ ํ ํฌ ์ ๋ฌ ์ค์ฐจ๊ฐ ์ค์ด๋ค๊ณ ์์
์ฑ๋ฅ์ด ํฌ๊ฒ ํฅ์๋ ์ ์๋ค. ๋ณธ ํ์ ๋
ผ๋ฌธ์ ์ฐ๊ตฌ ๊ฒฐ๊ณผ๋ค์ 12์์ ๋ ์ด์กฑ ๋ณดํ ๋ก๋ด DYROS-RED์ 7์์ ๋ ๋ก๋ด ํ Franka Emika Panda๋ฅผ ์ด์ฉํ ์คํ์ ํตํด ๊ฒ์ฆ๋์๋ค.1 INTRODUCTION 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contributions of Thesis . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Overview of Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 BACKGROUNDS 6
2.1 Operational Space Control . . . . . . . . . . . . . . . . . . . . . . 6
2.2 Dynamics Formulation . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.1 Fixed-Base Dynamics . . . . . . . . . . . . . . . . . . . . 9
2.2.1.1 Joint Space Formulation . . . . . . . . . . . . . 9
2.2.1.2 Operational Space Formulation . . . . . . . . . . 11
2.2.2 Floating-Base Dynamics . . . . . . . . . . . . . . . . . . . 12
2.2.2.1 Joint Space Formulation . . . . . . . . . . . . . 12
2.2.2.2 Operational Space Formulation . . . . . . . . . . 14
2.3 Position Tracking via PD Control . . . . . . . . . . . . . . . . . . 17
2.3.1 Torque Solution . . . . . . . . . . . . . . . . . . . . . . . 17
2.3.2 Orientation Control . . . . . . . . . . . . . . . . . . . . . 19
3 CONTACT-CONSISTENT DISTURBANCE OBSERVER FOR FLOATING-BASE ROBOTS 22
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.2 Momentum-Based Disturbance Observer . . . . . . . . . . . . . . 24
3.3 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 25
3.4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3.4.2 External Force Estimation . . . . . . . . . . . . . . . . . . 33
3.4.3 Internal Disturbance Rejection . . . . . . . . . . . . . . . 35
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4 OPERATIONAL SPACE CONTROL UNDER ACTUATOR BANDWIDTH LIMITATION 40
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
4.2 The Proposed Method . . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.1 General Concepts . . . . . . . . . . . . . . . . . . . . . . . 43
4.2.2 OSF-Based Torque Solution . . . . . . . . . . . . . . . . . 45
4.2.3 Comparison With a Typical Method . . . . . . . . . . . . 47
4.3 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
4.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
4.4 Comparison With Other Approaches . . . . . . . . . . . . . . . . 61
4.4.1 Controller Formulation . . . . . . . . . . . . . . . . . . . . 62
4.4.1.1 The Proposed Method . . . . . . . . . . . . . . . 62
4.4.1.2 The OSF Controller . . . . . . . . . . . . . . . . 62
4.4.1.3 The OSF-Filter Controller . . . . . . . . . . . . 62
4.4.1.4 The OSF-Joint Controller . . . . . . . . . . . . . 67
4.4.1.5 The Joint Controller . . . . . . . . . . . . . . . . 68
4.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
4.5 Frequency Response of Joint Torque . . . . . . . . . . . . . . . . 72
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
5 CONCLUSION 85
Abstract (In Korean) 100๋ฐ
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